Instructions to use kthakar/mistral-finetuned-kedar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use kthakar/mistral-finetuned-kedar with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("TheBloke/Mistral-7B-Instruct-v0.1-GPTQ") model = PeftModel.from_pretrained(base_model, "kthakar/mistral-finetuned-kedar") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files
README.md
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The following hyperparameters were used during training:
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- learning_rate: 0.0002
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- train_batch_size:
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- eval_batch_size: 8
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- training_steps:
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- mixed_precision_training: Native AMP
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### Training results
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The following hyperparameters were used during training:
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- learning_rate: 0.0002
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- training_steps: 2000
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- mixed_precision_training: Native AMP
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### Training results
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